Such a diagnostic device and diagnostic method is known from the patent publication U.S. Pat. No. 6,601,005. The device senses process variable values like, e.g., pressure or flow values, and processes them so as to extract from them vibration noise signals carried in a process medium (e.g., liquid, gas) of the process. Such a vibration-related processed signal is then evaluated, with the evaluation ending in an output indicating that some control device (e.g., a pump or valve) of the process has a failure.
In the method disclosed in the afore-mentioned U.S. Pat. No. 6,601,005 it is suggested to use wavelet or Fourier transform or a neural network or statistical analysis, or other signal evaluation techniques for obtaining the processed signal. As far as the evaluation of the processed signal is concerned, it is suggested to compare the processed signal or signals to a choosable limit value or a set of limit values. For example, if a wavelet transform has been used for obtaining the processed signal, it will be checked for each calculated wavelet coefficient, if it has exceeded a corresponding limit value. If at least one wavelet coefficient has exceeded its corresponding limit value, a failure (of some control device or devices, to which the wavelet coefficient relates) will be indicated.
Furthermore, it is disclosed in the afore-mentioned U.S. Pat. No. 6,601,005 to remove known process variations, which can be due to certain process activities, from the process variable values. This is done by subtracting modeled data from data (process variable values) gathered during operation. It is expected that after the subtraction only abnormalities remain and are evaluated. If, e.g., the known process variations are due to environmental temperature changes, which occur during the day and which influence the process variable values, a number of data sets can be taken at different times during the day when the process works failure-free. These base “plane” normal operation data sets are employed as modeled data sets, from which, e.g., a neural network can choose the appropriate one, which then is substracted from data gathered during operation so as to let only abnormal signals remain. The models and the data gathered during operation are both wavelet transformed data, so that corresponding wavelet coefficients are subtracted from each other in order to remove the known process variations and yield values to be compared to prescribable limit values.
The choice of the limit values to which a processed signal is compared, is very important for the reliability of the output of the diagnostic device (“failure”/“no failure”). It is desirable to provide for reliable grounds for the diagnostic output of the diagnostic device.